bwca.dpcoa {ade4} | R Documentation |
Between- and within-class double principal coordinate analysis
Description
These functions allow to study the variations in diversity among communities (as in dpcoa) taking into account a partition in classes
Usage
bwca.dpcoa(x, fac, cofac, scannf = TRUE, nf = 2, ...)
## S3 method for class 'dpcoa'
bca(x, fac, scannf = TRUE, nf = 2, ...)
## S3 method for class 'dpcoa'
wca(x, fac, scannf = TRUE, nf = 2, ...)
## S3 method for class 'betwit'
randtest(xtest, nrepet = 999, ...)
## S3 method for class 'betwit'
summary(object, ...)
## S3 method for class 'witdpcoa'
print(x, ...)
## S3 method for class 'betdpcoa'
print(x, ...)
Arguments
x |
an object of class |
fac |
a factor partitioning the collections in classes |
scannf |
a logical value indicating whether the eigenvalues barplot should be displayed |
nf |
if scannf FALSE, a numeric value indicating the number of kept axes |
... |
further arguments passed to or from other methods |
cofac |
a cofactor partitioning the collections in classes used as a covariable |
nrepet |
the number of permutations |
xtest , object |
an object of class |
Value
Objects of class betdpcoa
, witdpcoa
or betwit
Author(s)
Stéphane Dray stephane.dray@univ-lyon1.fr
References
Dray, S., Pavoine, S. and Aguirre de Carcer, D. (2015) Considering external information to improve the phylogenetic comparison of microbial communities: a new approach based on constrained Double Principal Coordinates Analysis (cDPCoA). Molecular Ecology Resources, 15, 242–249. doi:10.1111/1755-0998.12300
See Also
Examples
## Not run:
## First example of Dray et al (2015) paper
con <- url("https://pbil.univ-lyon1.fr/datasets/dray/MER2014/soilmicrob.rda")
load(con)
close(con)
## Partial CCA
coa <- dudi.coa(soilmicrob$OTU, scannf = FALSE)
wcoa <- wca(coa, soilmicrob$env$pH, scannf = FALSE)
wbcoa <- bca(wcoa,soilmicrob$env$VegType, scannf = FALSE)
## Classical DPCoA
dp <- dpcoa(soilmicrob$OTU, soilmicrob$dphy, RaoDecomp = FALSE, scannf = FALSE)
## Between DPCoA (focus on the effect of vegetation type)
bdp <- bca(dp, fac = soilmicrob$env$VegType , scannf = FALSE)
bdp$ratio ## 0.2148972
randtest(bdp) ## p = 0.001
## Within DPCoA (remove the effect of pH)
wdp <- wca(dp, fac = soilmicrob$env$pH, scannf = FALSE)
wdp$ratio ## 0.5684348
## Between Within-DPCoA (remove the effect of pH and focus on vegetation type)
wbdp <- bwca.dpcoa(dp, fac = soilmicrob$env$VegType, cofac = soilmicrob$env$pH, scannf = FALSE)
wbdp$ratio ## 0.05452813
randtest(wbdp) ## p = 0.001
## End(Not run)